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US 11,531,906 B2Granted
Machine-learning-based processing of de-obfuscated data for data enrichment
AssigneeOracle International Corporation
InventorsTara U. Roberts, Jonathan Stanesby, Michael Pearmain, Kevin Yordy, Karen Sutthakorn
Life of this patent
Estimating the term…
Abstract
Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.
Claims20
A computer-implemented method comprising:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifier of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- executing a trained machine-learning model to process at least part of the user data corresponding to the non-obfuscated version of the profile that includes the PII;
- identifying one or more communication specifications based on the execution of the trained machine-learning model;
- causing a first content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications, wherein a time of the transmission of the first content, a type of communication transmission used for the transmission of the first content and/or part or all of the first content correspond to the one or more communication specifications;
- receiving one or more new communications, each of the one or more new communications having been transmitted from a user device in response to the transmission of the first content, wherein the user device is associated with the destination address;
- extracting field values from the one or more new communications, wherein the extracted field values identify new user-activity data collected from the one or more new communications;
- causing a second content to be transmitted to the destination address, wherein the second content includes the extracted field values from the one or more new communications;
- receiving, from the user device and in response to the transmission of the second content, one or more indications as to which of the extracted field values added information to the obfuscated versions of the profiles associated with the set of obfuscated identifiers;
- and
- further training an implementation of the trained machine-learning model using the one or more indications.
11 more elements
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifier of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- executing a trained machine-learning model to process at least part of the user data corresponding to the non-obfuscated version of the profile that includes the PII;
- identifying one or more communication specifications based on the execution of the trained machine-learning model;
- causing a first content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications, wherein a time of the transmission of the first content, a type of communication transmission used for the transmission of the first content and/or part or all of the first content correspond to the one or more communication specifications;
- receiving one or more new communications, each of the one or more new communications having been transmitted from a user device in response to the transmission of the first content, wherein the user device is associated with the destination address;
- extracting field values from the one or more new communications, wherein the extracted field values identify new user-activity data collected from the one or more new communications;
- causing a second content to be transmitted to the destination address, wherein the second content includes the extracted field values from the one or more new communications;
- receiving, from the user device and in response to the transmission of the second content, one or more indications as to which of the extracted field values added information to the obfuscated versions of the profiles associated with the set of obfuscated identifiers;
- and
- further training an implementation of the trained machine-learning model using the one or more indications.
11 more elements
A system comprising:
- one or more data processors;
- and
- a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifier of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- executing a trained machine-learning model to process at least part of the user data corresponding to the non-obfuscated version of the profile that includes the PII;
- identifying one or more communication specifications based on the execution of the trained machine-learning model;
- causing a first content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications, wherein a time of the transmission of the first content, a type of communication transmission used for the transmission of the first content and/or part or all of the first content correspond to the one or more communication specifications;
- receiving one or more new communications, each of the one or more new communications having been transmitted from a user device in response to the transmission of the first content, wherein the user device is associated with the destination address;
- extracting field values from the one or more new communications, wherein the extracted field values identify new user-activity data collected from the one or more new communications;
- causing a second content to be transmitted to the destination address, wherein the second content includes the extracted field values from the one or more new communications;
- receiving, from the user device and in response to the transmission of the second content, one or more indications as to which of the extracted field values added information to the obfuscated versions of the profiles associated with the set of obfuscated identifiers;
- and
- further training an implementation of the trained machine-learning model using the one or more indications.
14 more elements
Dependencies are inferred from the claim text.